For scalable indoor positioning systems, they are required to be based on an existing infrastructure in the buildings and on existing capabilities in the consumer devices. Therefore, indoor positioning systems should be based on radio-based technologies, e.g. WiFi- and/or Bluetooth technologies.
One approach for such radio-based indoor positioning is to model the radio environment (e.g. WiFi- and/or Bluetooth environment) from observed Received Signal Strength (RSS) measurements, which are generated into a 2-dimensional radio map. The radio map is used as navigation data and represents for instance the dynamics of the indoor radio propagation environment, e.g. represented by radio models of radio nodes in the building. Based on this information, determining of an indoor position can be achieved in a highly accurate way within the coverage of the generated radio map.
For acquiring data necessary for generating the radio map, huge volumes of indoor radio data by measurements could be harvested e.g. via crowd-sourcing by electronic devices of consumers, which are equipped with the necessary functionality to enable for instance the data collection as a background process, naturally with the end-user consent.
The radio map is generated from harvested data, which is collected from all areas within a building, where localization functionality is needed, during sufficiently short period of time. If data is harvested from all areas within the building, which is collected during different times, or if data is harvested from partial areas within the building, e.g. only of a specific floor, incomplete radio models may occur in the generated radio map. This may have badly impact on localization performance of such indoor positioning systems.
It is an object of the disclosure to detect incomplete radio models and process data associated with incomplete radio models of a generated radio map properly.